Sunday, August 23, 2015

Does having a state-run exchange improve health insurance access under the ACA?

With the ruling in King vs. Burwell behind us, focus on the
differences between state sponsored health exchanges vs. the federal exchange
has fallen away. But as state-based data has been rolling in from Gallup,
the CDC,
and the Urban Institute
on declines in people without health insurance, I began wondering whether
providing a state-based exchange has any advantages over a the federal
marketplace. Gallup’s comments and tables in particular seem to push the idea that states with state-based
exchanges seem to have had more success with reducing the uninsured rate.

Of course, the real reason that state-based exchanges exist is
political. The original House of Representatives bill had a national
marketplace, while the Senate Bill incorporated state-based marketplaces. This
particular breakdown shouldn’t surprise anyone, since Senators represent entire
states, and all states are equally represented. The general state-based
structure of the Senate bill won out. The final ACA incorporated a mechanism
that defaulted to a federal backstop, but the markets themselves were still
based on state boundaries.

However, despite that structural political reason, there
might be some practical reasons why state exchanges might have superior
performance to a unified federal exchange. First, commentators often refer to
states as “laboratories of democracies” that can innovate and try numerous
different ideas. Over time, the theory goes, good ideas from some states will
diffuse across other states naturally and more quickly than if the federal
government had installed and tried to improve a clunky national idea.Second, there’s the idea that differing
conditions and preferences across states mean that state-based exchanges will allow individual
states to customize their exchanges to best fit the needs of their state.

We would have to hold these potential advantages against
some very real drawbacks. First, there’s administrative complexity and cost of
constructing and running an exchange for a state-level. With federal grants to
construct exchanges running out, several smaller states are already
transitioning back to the federal marketplace, while others are having trouble paying the upkeep costs. Also note that there’s a question of whether several
states even have enough potential subscribers to form a healthy individual
insurance market to begin with.

With these ideas in mind, I used Gallup’s state-based data
to build an extremely simple statistical model to predict the effects of a
state-based exchange on improvements in health insurance coverage. Follow me below the fold for more details.

The next four paragraphs and the chart describe the model in
some detail. If you want to cut to the executive summary written in English,
skip the next four paragraphs.

This Section Has the Wonky Methods Explanation and
Statistics Stuff.

The dependent variable (what we’re trying to predict) is the
percentage decrease in the uninsured population in each state according to
Gallup’s polling between 2013 and the first half of 2015. Note that this
measure is the percentage decrease, not the percentage point decrease. So
Massachusetts, whose uninsured rate declined from 4.9 percent to 3.0 percent,
would show a reduction of 39 percent , not 1.9 percentage points. The reason to
employ this measure is toavoid the bias
that accrues by some states have the ability to have a much larger percentage
point decrease because they started out with a far greater proportion of their population
uninsured.

My model includes two independent variables. The first
represents whether or not the state implemented the Medicaid expansion by the
Jan. 1 2015, with states implementing it scoring 1, and those that didn’t
scoring 0.For the purposes of the
exercise, I counted Wisconsin as expanding Medicaid, since they extended
Medicaid coverage to all people underneath 100 percent of the poverty line
(while cutting it for those over, but they still have access to heavily
subsidized insurance).The other
variable, the one of interest here, is what type of exchange a state had in
2014 (according to the Kaiser Foundation). States with a full-state
exchange scored a 2, states with a partnership scored a 1, and states relying
on the federal marketplace were a zero.

I regress the reduction in state percentage of the uninsured
on the Medicaid expansion and State Exchange variables using Ordinary Least
Squares Regression (the simplest form of regression). If having a state
exchange helps reduce the uninsured rate while controlling for the effects of
the Medicaid expansion, we should see a positive coefficient on the exchange
variable. Basically, this is a three-dimensional version of your basic y=m(x)+b
slope-intercept formula you might remember from your first-year algebra class
(So it’s really y=m(x1) +m(x2) +b). Regression takes all the points on the graph
(which are the Gallup results for the states) and finds the best-fit line
through the data. The slope of that line estimates the size of the effect that
each independent variable (the presence or non-presence of Medicaid expansion
and the State-based exchanges) on the dependent variable (the percent change in
the uninsured rate). Where the line “misses” each point, the regression notes the size of the miss and records the distance of the "miss" as error.

Table 1 shows the results. Overall, the two independent
variables explain about 28 percent of the variation in the Gallup data, which
is quite a high for only a two-variable model in cross-sectional data.

Both
expanding Medicaid and having a state exchange seem to have a positive effect.
The Medicaid expansion has the largest effect, increasing the percent decline
by 15.5 points. Having a state exchange would increase the percent decrease by
5.1 points (having a partnership exchange would increase it about 2.5 points.) Think
of the constant (which is the Y-intercept) as the estimated “baseline” decrease
before calculating the effect of the I
do not report standard errors here, because this analysis incorporates every
state, not a subsample of them.

Finally note while I report on all 50 states,
there is a source of error from the Gallup data while derives its estimates
from a sample people in each state. Most of these samples are quite large,
holding the margin of error down (assuming the sampling was done correctly),
but some of the smaller states have samples of several hundred people, which
leaves a larger margin of error (See Charles Gabba, aka "Brainwrap" at ACA, for more on this.)
Having said this, the Gallup estimates seem to track very similar to other data
points.

Table 1: Influence of Having a State-Based Exchange

and Medicaid Expansion on % Decline in the Uninsured Rate

Variable Name

Coefficient

Medicaid Expansion:

15.51

State-based Exchange

2.55

Constant

26.46

N= 50

Adjusted R-squared =.2811

Plain English Explanation:

The regression results in table 1 show that having a state
exchange decreases the uninsured rate by a modest amount (about 5.1 percent) In
contrast, expanding Medicaid has a large effect on decreasing the uninsured
rate (about 15.5 percent).Both together
have a large effect on the effectiveness of reducing the uninsured.

Here’s a practical illustration using the Gallup data: The
median state had a 34.6 percent reduction in its uninsured rate between the end
of 2013 and 2015.A state in the 25th percentile
had a 26.9 percent reduction, while a state in the 75th percentile had a
reduction of 48.4 percent.The upshot is
that a state performing in the 25th percentile without having its own exchange nor
expanding Medicaid would have performed in the 75 percentile if it had done
both of those things (Or think of it as the equivalent of increasing your ACT
score by eight points).

Again, however, note that the lion’s share of the effect comes
from expanding Medicaid, not having a state-run exchange. We also have a pretty good idea of how the
Medicaid expansion increases coverage (it provides more money to get people
covered) In contrast, the small effect that having state exchange has might be
a statistical artifact: After all, states that deployed a state-based exchange
were also aggressively implementing Obamacare in other ways as well, like more
and better navigators, public-awareness campaigns and like.Perhaps the improvement that the model shows
in the state-run exchange really is a reflection of these factors.

Bottom line: does having a state-exchange help improve the
ACA’s performance?The answer: It might
help a little, but the effect is probably overstated in this simple model, and it is far overshadowed the the effect of Medicaid.

Next time, I'll build a slightly different model to explore how much accepting the Medicaid expansion might help reduce the uninsured rate.